Real-Time Drowsiness Detection System for Student Tracking using Machine Learning

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Published Feb 15, 2023
https://doi.org/10.47164/ijngc.v14i1.992
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Volume 14, Special Issue 1, February 2023

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Dilipkumar A. Borikar
Shri Ramdeobaba College of Engineering and Management, Nagpur
https://orcid.org/0000-0001-7966-3402
Himani Dighorikar
Shri Ramdeobaba College of Engineering and Management, Nagpur
Shridhar Ashtikar
Shri Ramdeobaba College of Engineering and Management, Nagpur
Ishika Bajaj
Shri Ramdeobaba College of Engineering and Management, Nagpur, INDIA
Shivam Gupta
Shri Ramdeobaba College of Engineering and Management, Nagpur

Abstract

Many studies on fatigue detection have been carried out that were focused on experimention over different technologies. Machine vision based driver fatigue detection systems are used to prevent accidents and improve safety on roads. We propose the design of an alerting system for the students that will use real time video of a person to capture the drowsiness level and will signal alert to the student when the student is in that state of fatigue. A device, if enabled with the system, will start the webcam and track the person. An alert will be generated based on the set frame rate when a continuous set of frames are detected as drowsy. The  conventional methods cannot capture complex expressions, however the vailability of deep learning models has enabled a substantial research on detection of states of a person in real time. Our system operates in natural lighting conditions and can predict accurately even when the face is covered with glasses, head caps, etc. The system is implemented using YOLOv5 models (You Look Only Once) is an extremely fast and accurate detection model.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biographies

Dilipkumar A. Borikar, Shri Ramdeobaba College of Engineering and Management, Nagpur

Department of Computer Science and Engineering / Assistant Professor

Himani Dighorikar, Shri Ramdeobaba College of Engineering and Management, Nagpur

Department of Computer Science and Engineering / Student

Shridhar Ashtikar, Shri Ramdeobaba College of Engineering and Management, Nagpur

Department of Computer Science and Engineering / Student

Ishika Bajaj, Shri Ramdeobaba College of Engineering and Management, Nagpur, INDIA

Department of Computer Science and Engineering / Student

Shivam Gupta, Shri Ramdeobaba College of Engineering and Management, Nagpur

Department of Computer Science and Engineering / Student

How to Cite
Dilipkumar Borikar, Himani Dighorikar, Shridhar Ashtikar, Ishika Bajaj, & Shivam Gupta. (2023). Real-Time Drowsiness Detection System for Student Tracking using Machine Learning. International Journal of Next-Generation Computing, 14(1). https://doi.org/10.47164/ijngc.v14i1.992
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